标题： 基于表面pH数据的贝叶斯字典学习细胞膜渗透性估计 显示英文标题

标题： Bayesian dictionary learning estimation of cell membrane permeability from surface pH data

摘要： 气体通过细胞膜的运输是生物化学中一个非常重要的过程，对于许多关键任务至关重要，包括细胞呼吸和细胞内的pH调节。 在20世纪90年代末，有建议称气体是通过嵌入细胞膜中的优先气体通道进行运输的，这挑战了已有百年历史的奥弗顿理论，该理论认为气体通过脂质细胞膜沿着浓度梯度扩散。 由于仅凭实验证据不足以支持所提出的机制之一，因此引入了数学模型，以提供对实验室测量结果解释的背景。 在之前的工作中，膜渗透性是使用粒子滤波器估计的，本文我们提出了一种基于字典学习的算法来估计细胞膜的渗透性。 计算示例表明，这种新方法在膜的特性在数据收集过程中不发生变化的情况下可以应用，并且比粒子滤波器在计算上要高效得多。 显示英文摘要

摘要： Gas transport across cell membrane is a very important process in biochemistry which is essential for many crucial tasks, including cell respiration pH regulation in the cell. In the late 1990's, the suggestion that gasses are transported via preferred gas channels embedded into the cell membrane challenged the century old Overton's theory that gases pass through the lipid cell membrane by diffusing across the concentration gradient. Since experimental evidence alone does not provide enough evidence to favor one of the proposed mechanisms, mathematical models have been introduced to provide a context for the interpretation of laboratory measurement. Following up on previous work where the membrane permeability was estimated using particle filter, in this article we propose an algorithm based on dictionary learning for estimating cell membrane permeability. Computed examples illustrate that the novel approach, which can be applied when the properties of the membrane do not change in the course of the data collection process, is computationally much more efficient than particle filter.